ArticlePLoS computational biology2018
A maximum-entropy model for predicting chromatin contacts.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Minimax entropy: The statistical physics of optimal models.Physical review. E · 2025Article
- A maximum-entropy model to predict 3D structural ensembles of chromatin from pairwise distances with applications to interphase chromosomes and structural variants.Nature communications · 2023Article
- Maximum Entropy Technique and Regularization Functional for Determining the Pharmacokinetic Parameters in DCE-MRI.Journal of digital imaging · 2022Article
- Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation.Nature communications · 2022Article
- Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis.Entropy (Basel, Switzerland) · 2022Article
- Dense neural networks for predicting chromatin conformation.BMC bioinformatics · 2018Article
- An introduction to the maximum entropy approach and its application to inference problems in biology.Heliyon · 2018Review
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2 authors.
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Abstract
The packaging of DNA inside a nucleus shows complex structure stabilized by a host of DNA-bound factors. Both the distribution of these factors and the contacts between different genomic locations of the DNA can now be measured on a genome-wide scale. This has advanced the development of models aimed at predicting the conformation of DNA given only the locations of bound factors-the chromatin folding problem. Here we present a maximum-entropy model that is able to predict a contact map representation of structure given a sequence of bound factors. Non-local effects due to the sequence neighborhood around contacting sites are found to be important for making accurate predictions. Lastly, we show that the model can be used to infer a sequence of bound factors given only a measurement of structure. This opens up the possibility for efficiently predicting sequence regions that may play a role in generating cell-type specific structural differences.
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